The goal of this proposal is to develop nonlinear models of athetoid movement and to use these models in filtering algorithms to assist computer access for persons with athetoid cerebral palsy (CP). Athetosis is a major barrier to computer usage (and therefore to general independence) for persons with cerebral palsy and other diseases. It is a complex movement disorder in which studies have identified multiple components of involuntary motion, including both quasi-periodic (i.e., tremor) and aperiodic components. Linear filtering is of limited benefit, since athetosis both reduces the bandwidth of purposeful movement and corrupts the remaining bandwidth with involuntary movement at the same frequencies. In this research, a nonlinear method for estimating intended computer input by persons with athetosis will be developed. The research will proceed as follows: 1. Human subjects with athetoid CP will use a joystick to execute a series of computer tasks without any input filtering. Subjects will complete a series of target acquisition or "icon-clicking" tasks using a novel isometric joystick that is well suited for athetoid users. The joystick readings and screen cursor positions will be recorded. 2. Neural networks will be used to develop nonlinear black-box models of the mapping between intended and actual movement during these experiments. A versatile neural network structure, the cascade-correlation learning architecture, will be used for modeling. 3. The models developed will be validated using recorded data. Validation will be performed using a stochastic similarity measure that is well suited to evaluation of human performance modeling data. 4. The validated models will be implemented online as input filters and will then be field- tested by the athetoid subjects in realistic computer tasks. Subjects will complete target acquisition trials both with and without filtering, and the performance results will be compared statistically. [unreadable] [unreadable] [unreadable] [unreadable]